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de Godoy, L. M. F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).
Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).
Nagaraj, N. et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol. Cell. Proteomics 11, M111.013722 (2012).
Kim, M.-S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).
Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014).
Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599 (2017).
Weiss, M., Schrimpf, S., Hengartner, M. O., Lercher, M. J. & von Mering, C. Shotgun proteomics data from multiple organisms reveals remarkable quantitative conservation of the eukaryotic core proteome. Proteomics 10, 1297–1306 (2010).
Marx, H. et al. A proteomic atlas of the legume Medicago truncatula and its nitrogen-fixing endosymbiont Sinorhizobium meliloti. Nat. Biotechnol. 34, 1198–1205 (2016).
Shendure, J. et al. DNA sequencing at 40: past, present and future. Nature 550, 345–353 (2017); correction Nature 568, E11 (2019).
Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 11, 319–324 (2014).
Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).
De Beeck, J. O. et al. Digging deeper into the human proteome: a novel nanoflow LCMS setup using micro pillar array columns (μPACTM). Preprint at bioRxiv https://doi.org/10.1101/472134 (2018).
Kulak, N. A., Geyer, P. E. & Mann, M. Loss-less nano-fractionator for high sensitivity, high coverage proteomics. Mol. Cell. Proteomics 16, 694–705 (2017).
Zhou, X.-X. et al. pDeep: predicting MS/MS spectra of peptides with deep learning. Anal. Chem. 89, 12690–12697 (2017).
Tiwary, S. et al. High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat. Methods 16, 519–525 (2019).
Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods 16, 509–518 (2019).
UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47 (D1), D506–D515 (2019).
Muñoz, J. & Heck, A. J. R. From the human genome to the human proteome. Angew. Chem. Int. Edn 53, 10864–10866 (2014).
Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).
Altenhoff, A. M. et al. Standardized benchmarking in the quest for orthologs. Nat. Methods 13, 425–430 (2016).
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47 (D1), D309–D314 (2019).
The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 47 (D1), D330–D338 (2019).
Geer, L. Y. et al. The NCBI BioSystems database. Nucleic Acids Res. 38, D492–D496 (2010).
El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47 (D1), D427–D432 (2019).
Santos, A. et al. Clinical knowledge graph integrates proteomics data into clinical decision-making. Preprint at bioRxiv https://doi.org/10.1101/2020.05.09.084897 (2020).
Cox, J. & Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13 (Suppl 16), S12 (2012).
Chi, H. et al. Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine. Nat. Biotechnol. 36, 1059–1061 (2018).
Zielinska, D. F., Gnad, F., Schropp, K., Wiśniewski, J. R. & Mann, M. Mapping N-glycosylation sites across seven evolutionarily distant species reveals a divergent substrate proteome despite a common core machinery. Mol. Cell 46, 542–548 (2012).
Wiśniewski, J. R., Wegler, C. & Artursson, P. Multiple-enzyme-digestion strategy improves accuracy and sensitivity of label- and standard-free absolute quantification to a level that is achievable by analysis with stable isotope-labeled standard spiking. J. Proteome Res. 18, 217–224 (2019).
Kelstrup, C. D. et al. Performance evaluation of the Q Exactive HF-X for shotgun proteomics. J. Proteome Res. 17, 727–738 (2018).
Scheltema, R. A. & Mann, M. SprayQc: a real-time LC-MS/MS quality monitoring system to maximize uptime using off the shelf components. J. Proteome Res. 11, 3458–3466 (2012).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Wichmann, C. et al. MaxQuant.Live enables global targeting of more than 25,000 peptides. Mol. Cell. Proteomics 18, 982–994 (2019).
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47 (D1), D442–D450 (2019).
Perkel, J. M. Why Jupyter is data scientists’ computational notebook of choice. Nature 563, 145–146 (2018).